Overview

Dataset statistics

Number of variables11
Number of observations55440
Missing cells61134
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory209.1 B

Variable types

Numeric9
Categorical2

Alerts

Country has a high cardinality: 231 distinct values High cardinality
Unnamed: 0 is highly correlated with YearHigh correlation
Year is highly correlated with Unnamed: 0High correlation
Energy_consumption is highly correlated with Energy_production and 3 other fieldsHigh correlation
Energy_production is highly correlated with Energy_consumption and 2 other fieldsHigh correlation
GDP is highly correlated with Energy_consumption and 2 other fieldsHigh correlation
Population is highly correlated with Energy_consumption and 2 other fieldsHigh correlation
Energy_intensity_per_capita is highly correlated with Energy_intensity_by_GDPHigh correlation
Energy_intensity_by_GDP is highly correlated with Energy_intensity_per_capitaHigh correlation
CO2_emission is highly correlated with Energy_consumptionHigh correlation
Unnamed: 0 is highly correlated with YearHigh correlation
Year is highly correlated with Unnamed: 0High correlation
Energy_consumption is highly correlated with Energy_production and 3 other fieldsHigh correlation
Energy_production is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
GDP is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
Population is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
CO2_emission is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
Unnamed: 0 is highly correlated with YearHigh correlation
Year is highly correlated with Unnamed: 0High correlation
Energy_consumption is highly correlated with Energy_production and 2 other fieldsHigh correlation
Energy_production is highly correlated with Energy_consumptionHigh correlation
GDP is highly correlated with Energy_consumption and 1 other fieldsHigh correlation
Population is highly correlated with GDPHigh correlation
Energy_intensity_per_capita is highly correlated with Energy_intensity_by_GDPHigh correlation
Energy_intensity_by_GDP is highly correlated with Energy_intensity_per_capitaHigh correlation
CO2_emission is highly correlated with Energy_consumptionHigh correlation
Unnamed: 0 is highly correlated with YearHigh correlation
Year is highly correlated with Unnamed: 0High correlation
Energy_consumption is highly correlated with Energy_production and 3 other fieldsHigh correlation
Energy_production is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
GDP is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
Population is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
CO2_emission is highly correlated with Energy_consumption and 3 other fieldsHigh correlation
Energy_consumption has 11153 (20.1%) missing values Missing
Energy_production has 11151 (20.1%) missing values Missing
GDP has 15414 (27.8%) missing values Missing
Population has 9426 (17.0%) missing values Missing
Energy_intensity_per_capita has 5082 (9.2%) missing values Missing
Energy_intensity_by_GDP has 5082 (9.2%) missing values Missing
CO2_emission has 3826 (6.9%) missing values Missing
Energy_consumption is highly skewed (γ1 = 23.92149736) Skewed
Energy_production is highly skewed (γ1 = 24.16400807) Skewed
CO2_emission is highly skewed (γ1 = 24.07524101) Skewed
Unnamed: 0 is uniformly distributed Uniform
Country is uniformly distributed Uniform
Energy_type is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Energy_consumption has 10995 (19.8%) zeros Zeros
Energy_production has 20002 (36.1%) zeros Zeros
Energy_intensity_per_capita has 5784 (10.4%) zeros Zeros
Energy_intensity_by_GDP has 11442 (20.6%) zeros Zeros
CO2_emission has 26954 (48.6%) zeros Zeros

Reproduction

Analysis started2022-06-19 08:44:22.842702
Analysis finished2022-06-19 08:45:05.736079
Duration42.89 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct55440
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27719.5
Minimum0
Maximum55439
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:06.083157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2771.95
Q113859.75
median27719.5
Q341579.25
95-th percentile52667.05
Maximum55439
Range55439
Interquartile range (IQR)27719.5

Descriptive statistics

Standard deviation16004.2938
Coefficient of variation (CV)0.5773658904
Kurtosis-1.2
Mean27719.5
Median Absolute Deviation (MAD)13860
Skewness0
Sum1536769080
Variance256137420
MonotonicityStrictly increasing
2022-06-19T11:45:06.421250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
369641
 
< 0.1%
369531
 
< 0.1%
369541
 
< 0.1%
369551
 
< 0.1%
369561
 
< 0.1%
369571
 
< 0.1%
369581
 
< 0.1%
369591
 
< 0.1%
369601
 
< 0.1%
Other values (55430)55430
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
554391
< 0.1%
554381
< 0.1%
554371
< 0.1%
554361
< 0.1%
554351
< 0.1%
554341
< 0.1%
554331
< 0.1%
554321
< 0.1%
554311
< 0.1%
554301
< 0.1%

Country
Categorical

HIGH CARDINALITY
UNIFORM

Distinct231
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.5 MiB
World
 
240
Netherlands Antilles
 
240
New Zealand
 
240
Nicaragua
 
240
Niger
 
240
Other values (226)
54240 

Length

Max length28
Median length23
Mean length9.402597403
Min length4

Characters and Unicode

Total characters521280
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorld
2nd rowWorld
3rd rowWorld
4th rowWorld
5th rowWorld

Common Values

ValueCountFrequency (%)
World240
 
0.4%
Netherlands Antilles240
 
0.4%
New Zealand240
 
0.4%
Nicaragua240
 
0.4%
Niger240
 
0.4%
Nigeria240
 
0.4%
Niue240
 
0.4%
North Korea240
 
0.4%
North Macedonia240
 
0.4%
Northern Mariana Islands240
 
0.4%
Other values (221)53040
95.7%

Length

2022-06-19T11:45:06.767323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
islands2400
 
3.2%
and1920
 
2.6%
saint1200
 
1.6%
former960
 
1.3%
new720
 
1.0%
u.s720
 
1.0%
germany720
 
1.0%
south720
 
1.0%
guinea720
 
1.0%
republic720
 
1.0%
Other values (255)64320
85.6%

Most occurring characters

ValueCountFrequency (%)
a74880
14.4%
i43440
 
8.3%
n41520
 
8.0%
e37200
 
7.1%
r32160
 
6.2%
o27600
 
5.3%
s20160
 
3.9%
19680
 
3.8%
t19200
 
3.7%
l18960
 
3.6%
Other values (48)186480
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter421200
80.8%
Uppercase Letter75840
 
14.5%
Space Separator19680
 
3.8%
Other Punctuation3360
 
0.6%
Dash Punctuation960
 
0.2%
Final Punctuation240
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a74880
17.8%
i43440
10.3%
n41520
9.9%
e37200
8.8%
r32160
 
7.6%
o27600
 
6.6%
s20160
 
4.8%
t19200
 
4.6%
l18960
 
4.5%
u18720
 
4.4%
Other values (17)87360
20.7%
Uppercase Letter
ValueCountFrequency (%)
S9120
 
12.0%
C5520
 
7.3%
B5520
 
7.3%
M5280
 
7.0%
G5040
 
6.6%
I4800
 
6.3%
T4800
 
6.3%
A4320
 
5.7%
N4080
 
5.4%
P3360
 
4.4%
Other values (15)24000
31.6%
Other Punctuation
ValueCountFrequency (%)
.2400
71.4%
,720
 
21.4%
/240
 
7.1%
Space Separator
ValueCountFrequency (%)
19680
100.0%
Dash Punctuation
ValueCountFrequency (%)
-960
100.0%
Final Punctuation
ValueCountFrequency (%)
240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin497040
95.3%
Common24240
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a74880
15.1%
i43440
 
8.7%
n41520
 
8.4%
e37200
 
7.5%
r32160
 
6.5%
o27600
 
5.6%
s20160
 
4.1%
t19200
 
3.9%
l18960
 
3.8%
u18720
 
3.8%
Other values (42)163200
32.8%
Common
ValueCountFrequency (%)
19680
81.2%
.2400
 
9.9%
-960
 
4.0%
,720
 
3.0%
/240
 
1.0%
240
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII520800
99.9%
None240
 
< 0.1%
Punctuation240
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a74880
14.4%
i43440
 
8.3%
n41520
 
8.0%
e37200
 
7.1%
r32160
 
6.2%
o27600
 
5.3%
s20160
 
3.9%
19680
 
3.8%
t19200
 
3.7%
l18960
 
3.6%
Other values (46)186000
35.7%
None
ValueCountFrequency (%)
ô240
100.0%
Punctuation
ValueCountFrequency (%)
240
100.0%

Energy_type
Categorical

UNIFORM

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.7 MiB
all_energy_types
9240 
coal
9240 
natural_gas
9240 
petroleum_n_other_liquids
9240 
nuclear
9240 

Length

Max length25
Median length13.5
Mean length13.5
Min length4

Characters and Unicode

Total characters748440
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowall_energy_types
2nd rowcoal
3rd rownatural_gas
4th rowpetroleum_n_other_liquids
5th rownuclear

Common Values

ValueCountFrequency (%)
all_energy_types9240
16.7%
coal9240
16.7%
natural_gas9240
16.7%
petroleum_n_other_liquids9240
16.7%
nuclear9240
16.7%
renewables_n_other9240
16.7%

Length

2022-06-19T11:45:07.121376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-19T11:45:07.700827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
all_energy_types9240
16.7%
coal9240
16.7%
natural_gas9240
16.7%
petroleum_n_other_liquids9240
16.7%
nuclear9240
16.7%
renewables_n_other9240
16.7%

Most occurring characters

ValueCountFrequency (%)
e101640
13.6%
l73920
9.9%
_73920
9.9%
a64680
8.6%
r64680
8.6%
n55440
 
7.4%
t46200
 
6.2%
u36960
 
4.9%
o36960
 
4.9%
s36960
 
4.9%
Other values (11)157080
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter674520
90.1%
Connector Punctuation73920
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e101640
15.1%
l73920
11.0%
a64680
9.6%
r64680
9.6%
n55440
 
8.2%
t46200
 
6.8%
u36960
 
5.5%
o36960
 
5.5%
s36960
 
5.5%
p18480
 
2.7%
Other values (10)138600
20.5%
Connector Punctuation
ValueCountFrequency (%)
_73920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin674520
90.1%
Common73920
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e101640
15.1%
l73920
11.0%
a64680
9.6%
r64680
9.6%
n55440
 
8.2%
t46200
 
6.8%
u36960
 
5.5%
o36960
 
5.5%
s36960
 
5.5%
p18480
 
2.7%
Other values (10)138600
20.5%
Common
ValueCountFrequency (%)
_73920
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII748440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e101640
13.6%
l73920
9.9%
_73920
9.9%
a64680
8.6%
r64680
8.6%
n55440
 
7.4%
t46200
 
6.2%
u36960
 
4.9%
o36960
 
4.9%
s36960
 
4.9%
Other values (11)157080
21.0%

Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.5
Minimum1980
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:08.193522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile1981.95
Q11989.75
median1999.5
Q32009.25
95-th percentile2017.05
Maximum2019
Range39
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.54350049
Coefficient of variation (CV)0.005773193543
Kurtosis-1.201501073
Mean1999.5
Median Absolute Deviation (MAD)10
Skewness0
Sum110852280
Variance133.2524035
MonotonicityIncreasing
2022-06-19T11:45:08.518643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
19801386
 
2.5%
19811386
 
2.5%
20021386
 
2.5%
20031386
 
2.5%
20041386
 
2.5%
20051386
 
2.5%
20061386
 
2.5%
20071386
 
2.5%
20081386
 
2.5%
20091386
 
2.5%
Other values (30)41580
75.0%
ValueCountFrequency (%)
19801386
2.5%
19811386
2.5%
19821386
2.5%
19831386
2.5%
19841386
2.5%
19851386
2.5%
19861386
2.5%
19871386
2.5%
19881386
2.5%
19891386
2.5%
ValueCountFrequency (%)
20191386
2.5%
20181386
2.5%
20171386
2.5%
20161386
2.5%
20151386
2.5%
20141386
2.5%
20131386
2.5%
20121386
2.5%
20111386
2.5%
20101386
2.5%

Energy_consumption
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct30460
Distinct (%)68.8%
Missing11153
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.537810796
Minimum-0.163437731
Maximum601.04049
Zeros10995
Zeros (%)19.8%
Negative208
Negative (%)0.4%
Memory size433.2 KiB
2022-06-19T11:45:08.915580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.163437731
5-th percentile0
Q10
median0.018381028
Q30.2094223795
95-th percentile3.313893286
Maximum601.04049
Range601.2039277
Interquartile range (IQR)0.2094223795

Descriptive statistics

Standard deviation15.45659646
Coefficient of variation (CV)10.05103911
Kurtosis706.4965549
Mean1.537810796
Median Absolute Deviation (MAD)0.018381028
Skewness23.92149736
Sum68105.02673
Variance238.9063741
MonotonicityNot monotonic
2022-06-19T11:45:09.357401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010995
 
19.8%
4.34 × 10-544
 
0.1%
0.00364733628
 
0.1%
0.00066077220
 
< 0.1%
0.00369748118
 
< 0.1%
0.0024020616
 
< 0.1%
0.00025756316
 
< 0.1%
0.00062652314
 
< 0.1%
0.00021261314
 
< 0.1%
2.09 × 10-514
 
< 0.1%
Other values (30450)33108
59.7%
(Missing)11153
 
20.1%
ValueCountFrequency (%)
-0.1634377311
< 0.1%
-0.1552090631
< 0.1%
-0.1379707231
< 0.1%
-0.1340611671
< 0.1%
-0.1206520591
< 0.1%
-0.1082944251
< 0.1%
-0.1054295181
< 0.1%
-0.0996224291
< 0.1%
-0.0972004131
< 0.1%
-0.0883152821
< 0.1%
ValueCountFrequency (%)
601.040491
< 0.1%
597.73658231
< 0.1%
584.20181631
< 0.1%
572.15923071
< 0.1%
567.32882181
< 0.1%
566.12994221
< 0.1%
561.09215061
< 0.1%
551.60038341
< 0.1%
540.4951271
< 0.1%
526.73046771
< 0.1%

Energy_production
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct20598
Distinct (%)46.5%
Missing11151
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean1.532700012
Minimum-1 × 10-39
Maximum611.5089686
Zeros20002
Zeros (%)36.1%
Negative6
Negative (%)< 0.1%
Memory size433.2 KiB
2022-06-19T11:45:09.788247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1 × 10-39
5-th percentile0
Q10
median0.000512197
Q30.112541005
95-th percentile3.835083374
Maximum611.5089686
Range611.5089686
Interquartile range (IQR)0.112541005

Descriptive statistics

Standard deviation15.30355613
Coefficient of variation (CV)9.984704125
Kurtosis720.5210157
Mean1.532700012
Median Absolute Deviation (MAD)0.000512197
Skewness24.16400807
Sum67881.75082
Variance234.1988302
MonotonicityNot monotonic
2022-06-19T11:45:10.322817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
020002
36.1%
2.09 × 10-531
 
0.1%
1.9 × 10-518
 
< 0.1%
1.95 × 10-516
 
< 0.1%
1.98 × 10-516
 
< 0.1%
0.00214619816
 
< 0.1%
2.06 × 10-515
 
< 0.1%
2.08 × 10-514
 
< 0.1%
0.00214578814
 
< 0.1%
0.00369748114
 
< 0.1%
Other values (20588)24133
43.5%
(Missing)11151
20.1%
ValueCountFrequency (%)
-1 × 10-396
 
< 0.1%
020002
36.1%
1 × 10-393
 
< 0.1%
1.86 × 10-181
 
< 0.1%
1 × 10-72
 
< 0.1%
2.98 × 10-72
 
< 0.1%
8.29 × 10-71
 
< 0.1%
9.51 × 10-71
 
< 0.1%
9.52 × 10-74
 
< 0.1%
9.54 × 10-73
 
< 0.1%
ValueCountFrequency (%)
611.50896861
< 0.1%
600.72872951
< 0.1%
578.24525441
< 0.1%
568.5582921
< 0.1%
564.58027591
< 0.1%
563.33124191
< 0.1%
554.1775531
< 0.1%
546.33360541
< 0.1%
534.18787261
< 0.1%
518.73165751
< 0.1%

GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6653
Distinct (%)16.6%
Missing15414
Missing (%)27.8%
Infinite0
Infinite (%)0.0%
Mean827.1441264
Minimum0.124958
Maximum127690.2471
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:10.816498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.124958
5-th percentile1.01847
Q19.73778
median47.7571
Q3263.6871
95-th percentile2030.632
Maximum127690.2471
Range127690.1221
Interquartile range (IQR)253.94932

Descriptive statistics

Standard deviation5981.703144
Coefficient of variation (CV)7.231754362
Kurtosis235.688449
Mean827.1441264
Median Absolute Deviation (MAD)45.24724
Skewness14.48246875
Sum33107270.8
Variance35780772.5
MonotonicityNot monotonic
2022-06-19T11:45:11.287242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177.630436
 
0.1%
36.368712
 
< 0.1%
1.2605212
 
< 0.1%
5.61712
 
< 0.1%
14.71512
 
< 0.1%
181.30612
 
< 0.1%
7.5953112
 
< 0.1%
10.649612
 
< 0.1%
17.349412
 
< 0.1%
15.851712
 
< 0.1%
Other values (6643)39882
71.9%
(Missing)15414
 
27.8%
ValueCountFrequency (%)
0.1249586
< 0.1%
0.1276246
< 0.1%
0.1356496
< 0.1%
0.1359026
< 0.1%
0.1366546
< 0.1%
0.136696
< 0.1%
0.1375986
< 0.1%
0.1384856
< 0.1%
0.1417366
< 0.1%
0.1450786
< 0.1%
ValueCountFrequency (%)
127690.24716
< 0.1%
124161.9946
< 0.1%
119854.32176
< 0.1%
115567.88016
< 0.1%
111935.89576
< 0.1%
108404.17996
< 0.1%
104859.44896
< 0.1%
101534.11266
< 0.1%
98369.03226
< 0.1%
94617.526446
< 0.1%

Population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7657
Distinct (%)16.6%
Missing9426
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean62630.20477
Minimum11.471
Maximum7714631.064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:11.787906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11.471
5-th percentile89.1382
Q11141.95
median6157.68
Q320042.9
95-th percentile118711.8
Maximum7714631.064
Range7714619.593
Interquartile range (IQR)18900.95

Descriptive statistics

Standard deviation456208.8203
Coefficient of variation (CV)7.284166194
Kurtosis178.4748082
Mean62630.20477
Median Absolute Deviation (MAD)5758.731
Skewness12.93661597
Sum2881866242
Variance2.081264878 × 1011
MonotonicityNot monotonic
2022-06-19T11:45:12.188833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.77612
 
< 0.1%
9941.112
 
< 0.1%
294.97612
 
< 0.1%
13066.512
 
< 0.1%
161.67612
 
< 0.1%
3453.6712
 
< 0.1%
1016012
 
< 0.1%
3734.3412
 
< 0.1%
11282.712
 
< 0.1%
3559.512
 
< 0.1%
Other values (7647)45894
82.8%
(Missing)9426
 
17.0%
ValueCountFrequency (%)
11.4716
< 0.1%
11.8216
< 0.1%
12.256
< 0.1%
12.7556
< 0.1%
13.3176
< 0.1%
13.9516
< 0.1%
14.6466
< 0.1%
15.396
< 0.1%
16.1556
< 0.1%
16.2076
< 0.1%
ValueCountFrequency (%)
7714631.0646
< 0.1%
7632247.0126
< 0.1%
7548343.7896
< 0.1%
7464042.8466
< 0.1%
7379227.326
< 0.1%
7297269.9816
< 0.1%
7211822.2096
< 0.1%
7126262.2946
< 0.1%
7041712.6896
< 0.1%
6948810.0146
< 0.1%

Energy_intensity_per_capita
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7430
Distinct (%)14.8%
Missing5082
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean71.8989137
Minimum0
Maximum1139.320598
Zeros5784
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:12.625665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.79993914
median29.77925957
Q395.52362689
95-th percentile268.0845979
Maximum1139.320598
Range1139.320598
Interquartile range (IQR)91.72368775

Descriptive statistics

Standard deviation113.7287384
Coefficient of variation (CV)1.581786602
Kurtosis17.42699629
Mean71.8989137
Median Absolute Deviation (MAD)28.74208029
Skewness3.441978433
Sum3620685.496
Variance12934.22594
MonotonicityNot monotonic
2022-06-19T11:45:13.030591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05784
 
10.4%
68.145920816
 
< 0.1%
128.77967546
 
< 0.1%
36.748137576
 
< 0.1%
83.102441456
 
< 0.1%
8.930685976
 
< 0.1%
42.376129096
 
< 0.1%
493.48259676
 
< 0.1%
9.5233233586
 
< 0.1%
5.0774793776
 
< 0.1%
Other values (7420)44520
80.3%
(Missing)5082
 
9.2%
ValueCountFrequency (%)
05784
10.4%
0.3318476776
 
< 0.1%
0.3349775196
 
< 0.1%
0.3444533086
 
< 0.1%
0.3470767476
 
< 0.1%
0.3471987316
 
< 0.1%
0.3518580066
 
< 0.1%
0.3553123616
 
< 0.1%
0.3569646786
 
< 0.1%
0.3595328976
 
< 0.1%
ValueCountFrequency (%)
1139.3205986
< 0.1%
1127.2385536
< 0.1%
1115.454346
< 0.1%
1110.3201476
< 0.1%
1106.7928556
< 0.1%
1101.1797166
< 0.1%
1095.3583626
< 0.1%
1082.5940196
< 0.1%
1053.2227836
< 0.1%
1027.0443516
< 0.1%

Energy_intensity_by_GDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6486
Distinct (%)12.9%
Missing5082
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean3.695103863
Minimum0
Maximum166.9136046
Zeros11442
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size433.2 KiB
2022-06-19T11:45:13.433503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.899446174
median2.987592599
Q34.969454094
95-th percentile10.32595475
Maximum166.9136046
Range166.9136046
Interquartile range (IQR)4.07000792

Descriptive statistics

Standard deviation4.5907346
Coefficient of variation (CV)1.242383102
Kurtosis328.3645712
Mean3.695103863
Median Absolute Deviation (MAD)2.020993165
Skewness10.91730849
Sum186078.0403
Variance21.07484416
MonotonicityNot monotonic
2022-06-19T11:45:13.871333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011442
 
20.6%
1.34849587712
 
< 0.1%
4.6984889076
 
< 0.1%
5.516051096
 
< 0.1%
7.0423456646
 
< 0.1%
5.7483093836
 
< 0.1%
2.2119547976
 
< 0.1%
11.569837746
 
< 0.1%
4.4064319286
 
< 0.1%
3.6279066276
 
< 0.1%
Other values (6476)38856
70.1%
(Missing)5082
 
9.2%
ValueCountFrequency (%)
011442
20.6%
0.2312849876
 
< 0.1%
0.2327505346
 
< 0.1%
0.2332666146
 
< 0.1%
0.2492716256
 
< 0.1%
0.2511045376
 
< 0.1%
0.2536269066
 
< 0.1%
0.259266946
 
< 0.1%
0.265525716
 
< 0.1%
0.295209336
 
< 0.1%
ValueCountFrequency (%)
166.91360466
< 0.1%
153.40880356
< 0.1%
31.80738076
< 0.1%
31.756368746
< 0.1%
31.21894036
< 0.1%
31.144218576
< 0.1%
29.739225556
< 0.1%
29.598511516
< 0.1%
29.179889716
< 0.1%
29.159700486
< 0.1%

CO2_emission
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct16993
Distinct (%)32.9%
Missing3826
Missing (%)6.9%
Infinite0
Infinite (%)0.0%
Mean78.80008185
Minimum-0.005130222
Maximum35584.9335
Zeros26954
Zeros (%)48.6%
Negative3
Negative (%)< 0.1%
Memory size433.2 KiB
2022-06-19T11:45:14.285257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.005130222
5-th percentile0
Q10
median0
Q34.318822279
95-th percentile141.829587
Maximum35584.9335
Range35584.93863
Interquartile range (IQR)4.318822279

Descriptive statistics

Standard deviation902.2214629
Coefficient of variation (CV)11.44949906
Kurtosis711.3428978
Mean78.80008185
Median Absolute Deviation (MAD)0
Skewness24.07524101
Sum4067187.425
Variance814003.5681
MonotonicityNot monotonic
2022-06-19T11:45:14.718068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026954
48.6%
0.2495
 
0.9%
0.1355
 
0.6%
0.3317
 
0.6%
0.7232
 
0.4%
0.5227
 
0.4%
0.4214
 
0.4%
0.6192
 
0.3%
1154
 
0.3%
0.8139
 
0.3%
Other values (16983)22335
40.3%
(Missing)3826
 
6.9%
ValueCountFrequency (%)
-0.0051302221
 
< 0.1%
-0.003077471
 
< 0.1%
-0.0001383541
 
< 0.1%
026954
48.6%
1.97 × 10-71
 
< 0.1%
2.78 × 10-71
 
< 0.1%
8.24 × 10-71
 
< 0.1%
1.15 × 10-61
 
< 0.1%
1.23 × 10-61
 
< 0.1%
1.83 × 10-61
 
< 0.1%
ValueCountFrequency (%)
35584.93351
< 0.1%
35002.900781
< 0.1%
34894.260051
< 0.1%
34839.904251
< 0.1%
34751.605591
< 0.1%
34572.38071
< 0.1%
34423.414751
< 0.1%
33633.324491
< 0.1%
32519.305371
< 0.1%
30795.450941
< 0.1%

Interactions

2022-06-19T11:45:00.013434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:35.210900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:38.413171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:42.423452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:45.551095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:48.497217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:51.539084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:54.417398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:57.179013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:00.336569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:35.571783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:38.791162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:42.855299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:45.912128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:48.793462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:51.868210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:54.717590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:57.501147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:00.707582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:35.948763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:39.316756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:43.226310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:46.278152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:49.125538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:52.202317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:55.031785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:57.824309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:01.054648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:36.283865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:39.721677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:43.536489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:46.586330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:49.431719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:52.510489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:55.338928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:58.124483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:01.380781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:36.589054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:40.089693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:43.877589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:46.885526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:49.748871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:52.816705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:55.639127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:58.428674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:01.707908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:36.935127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:40.739955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:44.187736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:47.212651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:50.068018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:53.130848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:55.931347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:58.751808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:02.250453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:37.306134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:41.197729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:44.504890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:47.535786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:50.391177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:53.450978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:56.251487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:59.067976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:02.559625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:37.645226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:41.560758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:44.833013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:47.845961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:50.914759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:53.766162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:56.549695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:59.363174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:45:03.008428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:38.008254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:41.994599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:45.190057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:48.167098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:51.223932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:54.099259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:56.873827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-19T11:44:59.689300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-19T11:45:15.439142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-19T11:45:15.962746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-19T11:45:16.439472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-19T11:45:16.955089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-19T11:45:03.648714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-19T11:45:04.285013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-19T11:45:04.961152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-19T11:45:05.381063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0CountryEnergy_typeYearEnergy_consumptionEnergy_productionGDPPopulationEnergy_intensity_per_capitaEnergy_intensity_by_GDPCO2_emission
00Worldall_energy_types1980292.899790296.33722827770.910284298126.52268.14592110.5474946.627130
11Worldcoal198078.65613480.11419427770.910284298126.52268.14592110.5471409.790188
22Worldnatural_gas198053.86522354.76104627770.910284298126.52268.14592110.5471081.593377
33Worldpetroleum_n_other_liquids1980132.064019133.11110927770.910284298126.52268.14592110.5472455.243565
44Worldnuclear19807.5757007.57570027770.910284298126.52268.14592110.5470.000000
55Worldrenewables_n_other198020.70234420.77517827770.910284298126.52268.14592110.5470.000000
66Afghanistanall_energy_types19800.0265830.072561NaN13356.5001.9902830.000NaN
77Afghanistancoal19800.0024790.002355NaN13356.5001.9902830.000NaN
88Afghanistannatural_gas19800.0020940.062820NaN13356.5001.9902830.000NaN
99Afghanistanpetroleum_n_other_liquids19800.0146240.000000NaN13356.5001.9902830.000NaN

Last rows

Unnamed: 0CountryEnergy_typeYearEnergy_consumptionEnergy_productionGDPPopulationEnergy_intensity_per_capitaEnergy_intensity_by_GDPCO2_emission
5543055430Zambianatural_gas20190.0000000.000000247.181917873.8511.5865670.8378310.000000
5543155431Zambiapetroleum_n_other_liquids20190.0521350.000000247.181917873.8511.5865670.8378314.202188
5543255432Zambianuclear2019NaNNaN247.181917873.8511.5865670.8378310.000000
5543355433Zambiarenewables_n_other20190.1208110.123466247.181917873.8511.5865670.8378310.000000
5543455434Zimbabweall_energy_types20190.1686510.14346237.620414654.2011.5087014.4829628.964759
5543555435Zimbabwecoal20190.0450640.07596337.620414654.2011.5087014.4829624.586869
5543655436Zimbabwenatural_gas20190.0000000.00000037.620414654.2011.5087014.4829620.000000
5543755437Zimbabwepetroleum_n_other_liquids20190.0554980.00000037.620414654.2011.5087014.4829624.377890
5543855438Zimbabwenuclear2019NaNNaN37.620414654.2011.5087014.4829620.000000
5543955439Zimbabwerenewables_n_other20190.0680890.06749937.620414654.2011.5087014.4829620.000000